Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification. Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.

翻译：扩散模型近期已成功应用于视觉合成，能够生成极其逼真的图像。这引发了对其被恶意利用的严重担忧。本文提出采用最初为对抗样本检测开发的多重局部本征维数（multiLID）方法，用于自动检测合成图像并识别对应的生成器网络。与许多仅对GAN生成图像有效的现有检测方法不同，所提方法在多种实际应用场景中能达到近乎完美的检测效果。通过在已知和新创建数据集上的大量实验表明，所提multiLID方法在扩散检测与模型识别方面展现出显著优势。针对近期生成图像检测研究的实证评估常以"LSUN-Bedroom"数据集为主的问题，本文进一步构建了包含多种尺寸不同扩散模型样本的扩散生成图像检测综合基准。